The identification of effective and safe pharmaceutical, medical, agrochemical, biotech and genomics technologies is of great commercial and human importance. Currently, many potential medical innovations and pharmaceutical compounds which progress into the development phase are determined to be unsuitable for viable commercial use, being ineffective or inactive in humans or otherwise determined to be unsafe. The average cost of a new drug approval is between $360 and $600 million for each successful launch and requires from about 12 to 15 years to achieve. See Pharma Exec., January 2000, Windhover Information Prentis Grabowski, 1994 Journal of Health Economics, Vol. 13. Considering that only 3 in 10 drugs achieve revenues greater than their development costs, id, the unsuccessful selection of prospective drug products is extremely costly to the manufacturer, and ultimately to the insurer as well as to the consumer. It is thus an ongoing objective of the medical, biotechnology and pharmaceutical industries to find effective ways of reducing this high attrition rate.
There are many commercially available tools that utilize predictive models to eliminate unsuccessful products, such as drug compounds or the like, before substantial time and money are invested in research and development. One such model is used to predict adsorption, distribution, metabolism and excretion (ADME) properties and toxicology profiles of a drug compound. Once determined, a predictive ADME or ADME/tox model is useful for deciding on the particular courses of action to be taken in subsequent stages of the drug's development on the premise that drug candidates having superior ADME properties have a greater likelihood of clinical success. Examples of predictive ADME/tox tools are the BioPrint™ products distributed by Cerep, Inc., the VolSurf™ model by Tripos, iDEA™ from LION Bioscience AG and the QikProp™ software by Schrodinger, Inc.
While predictive models are helpful in determining clinically sound drug candidates, they only provide part of the overall picture. In particular there is a need to plan research work so as to best balance scientific and commercial/cost considerations. Such “business” related factors include the manner in which research operations are conducted, including determining the number of targets to be researched at any one time, which tests to apply to compounds that may become active ingredients in product candidates, the sequence in which to apply these tests, the criteria to apply when progressing compounds from one stage of research and development to the next, and whether certain compounds should be developed in parallel or serially with respect to each other. Another group of factors not considered by predictive scientific property models includes those dependent upon an organization's resource capacity and constraints, e.g., the scope and number of scientific personnel needed, the amount of lab space and equipment required, and the costs associated with each of these.
Other business considerations that are pivotal in ensuring a drug's ultimate commercial viability are the potential demand for treatment of a particular disease or condition, the available market size and competitors' activities related to treatment of the same condition or disease state to which the subject drug is targeted. The attractiveness of a drug, and hence the available market share, will depend on factors such as the frequency of dosing that are originally determined by the chemical nature of the compound chosen for development of an active ingredient, but cannot be directly measured in the early stages of research. Factors of this nature have to be projected from the emerging results of testing during the R&D process, and the planned tests may be modified in accordance with findings, possibly including the decision to work on a different active ingredient.
It is necessary to consider all of these business factors in combination with scientific factors to insure favorable risk-to-benefit and cost-to-profit ratios in the projects that discover, develop and commercialize a drug or other medical or biotechnology product. The fundamental problem is to capture and correctly apply an understanding of how early scientific measures of quality of a potential drug relate to economic measures of quality, i.e., sales performance and profitability, in the market.
A number of business modeling approaches exist to assist management in making the right decisions and best choices to increase the likelihood of the commercial success of a drug; however, they are not without their shortcomings. Two well-known business modeling approaches used in the pharmaceutical industry are throughput modeling and discrete event simulation.
Throughput modeling looks at how many compounds, leads and development candidates are expected to pass each stage, while discrete event simulation models the detail of tasks, sequence, time and contention for resources. A throughput model used in isolation is lacking in that it fails to assess how various decision criteria affect the quality of a drug compound and the yields that would be achieved in the later stages of testing.
The discrete event simulation approach is able to deal with fluctuations in work over time, which may be important in the later stages of R&D. See “A Systems Engineering Approach to New Product Development”, Gary Blau, CAST Communications, Vol. 20 No. 1, Summer 1997, pp 4-11. It is rare for such models, if applied to earlier stages of R&D, to represent in any depth the differences between individual examples of compounds or other research options, as these do not appear important from the viewpoint of scheduling a process such as screening as a materials handling operation. However, in reality, there are various dimensions of quality important in evaluating the potential commercial success of each of the many molecules that may need to be made and screened before identifying a development candidate, including, for example, activity, safety, transport properties and novelty. The presence or absence of these factors influences the value of the product and the cost and risk of downstream work. For example, molecules that show a lack of selectivity are less valuable as products, and also more likely to fail clinical trials, and lead series that enumerate only a small part of the variety of active chemical structures are more likely to lead to lost sales due to early launch by competitors of equally, or more, attractive products. In the reference cited above, many of these kinds of difference between options were combined into a single “degree of difficulty” affecting the time taken to work on a particular project. In a subsequent development of this approach, the sequence of decisions in drug development was combined with a representation of subjective success probabilities for different projects at different stages; however, it was acknowledged that the complexity and creativity inherent to the discovery process makes it difficult to capture all the activities in the discovery process. See “Risk Management in the Development of New Products in Highly Regulated Industries”, Blau et al., Computers and Chemical Engineering, Vol. 24, pp. 659-664 (2000).
Where a decision making process involves the impact of a single criterion or variable on the potential success of a product, combining cost and value parameters in a simple trade-off equation (cost-benefit analysis) provides reliable insights. However, such is not the case with multiple-attribute decision-making. While decision support methods do exist for analyzing multiple decision criteria, they are, too, not without shortcomings. In multi-attribute decision systems, the overall user preference or “utility” for an option is determined in terms of values of various attributes of the option and the preferences of the user towards each of those attributes (i.e., the importance of those attributes). A preference function combining these attributes and their values characterizes the structure of the preference model. See Keeney, R., Raiffa, H., Decisions with Multiple Objectives: Preferences and Value Trade-Offs, John Wiley and Sons, 1976. An alternative approach, conjoint analysis, has been used widely in market research to identify the factors that contribute to consumer preference, through a process of consumer research and data fitting (see www.populus.com/techpapers/conjoint.pdf). A drawback of both these methods, when applied to choice of research methods and research options, is the subjective nature of elicited human judgments on the utility of technical and scientific measurements, which may not be objectively based on business and economic metrics. For example, a scientist may, unwittingly, heavily weight a factor in the drug selection decision of which they have good knowledge (e.g., it is relevant to their scientific specialty) even if, objectively, such a factor is likely to make only a small contribution to value. Even the seemingly objective goal for a method of “accuracy” is misleading since shortfalls in predictive reliability have two components, false positives and false negatives, and the relative importance of these depends on the consequences of each, which in turn depends on the costs and value of downstream activities.
In order to successfully model the entirety of the R&D process with the aim of guiding its improvement, it is necessary to track the multiple sources of potential failure for each of the many research options, e.g., the screening of hundreds of thousands of compounds, through sequential stages of R&D where multiple criteria are used to select compounds, and where new research options are added through business processes such as lead optimization. The successful modeling of the full R&D process, in a way that takes account of uncertainty, the variety of research options, capacity constraints, and can incorporate new findings, has been an unsolved challenge.
Accordingly, there is still a need for comprehensive methods of improving the effectiveness of technology and scientific research in order to maximize the economic value of products that come out of such research, taking account of the operating costs and capacity of research. More particularly, there is a need for methods which take into consideration both scientific and business factors in the context of a research and development process having multiple measurable sources of variety (including the differing measures for screening results and also both descriptors and calculated values for structure and properties of compounds, series, targets and assays) and criteria (variables used as the basis for decision making) which are applied in sequence. While explicit probability distributions may be used with these approaches to model variation within such factors and correlations between them all, a large amount of data is required, which would be prohibitive if all possible combinations were included in the model. Methods are therefore required that can efficiently capture the essential results from compound screening and biological target evaluation in a way suitable for use in guiding subsequent decisions. It would be additionally advantageous if such methods provided for adaptive learning in which certain estimates of relationships between these variables, including variables usable as criteria, are adjusted or re-weighted in response to scientific and business outcomes, thereby refining the processes of product selection, research and development. Finally, to minimize user subjectivity, such methods should preferably relate scientific measures to business and economic outcomes using economic value as the common figure of merit.